Golang Job: AWS Cloud Engineer AI-ML Applications, SageMaker

Title: AWS Cloud Engineer AI/ML Applications, SageMaker
Location: Remote
Duration: 6+ Months
Rate: $55-60/hr W2 only
Visa: Only or s No fake profiles, 12+ years of genuine experience mandatory

TECHNICAL SKILLS

Must Have:

• AWS Services: Bedrock, SageMaker, ECS, Lambda

• Programming Languages: Demonstrated proficiency in Python and Golang

• AI/ML Expertise:

  • Experience implementing RAG (Retrieval-Augmented Generation) architectures

  • Experience using frameworks/tools like Transformers, PyTorch, TensorFlow, LangChain

  • Proficiency with Large Language Models (LLMs)

• Open Source: Demonstrated contributions to open-source AI/ML/Cloud projects

• Education: Ph.D. in AI/ML/Data Science

Nice to Have:

• Experience with AWS Organizations and Policy Guardrails (SCP, AWS Config)

• FinOps awareness (cost optimization in cloud/AI)

JOB DESCRIPTION

We are hiring a Distinguished Cloud AI Software Engineer who has actually built AI/ML applications not just read about them.

You will operate as a trusted advisor in a hands-on capacity for the development of retrieval-augmented generation (RAG) systems, fine-tuning LLMs, and AWS-native microservices that drive automation, insight, and governance in an enterprise environment. You ll design and deliver scalable, secure services that bring Large Language Models (LLMs) into real operational use connecting them to live infrastructure data, internal documentation, and system telemetry.

This is a high-impact team pushing the boundaries of cloud-native AI in a real-world enterprise setting. This is not a prompt-engineering sandbox or a resume keyword trap. If you ve merely dabbled in SageMaker, mentioned RAG on LinkedIn, or read about vector search this isn t the right fit. We re looking for candidates who have architected, developed, and supported AI/ML services in production environments.

This is a builder s role within our Public Cloud AWS Engineering team. We aren t hiring buzzword lists or conference attendees. If you ve built something you re proud of especially if it involved real infrastructure, real data, and real users we d love to talk. If you re still learning, that s great too but this isn t an entry-level role or a theory-only position.

DUTIES AND RESPONSIBILITIES

• Design, develop, and maintain modular AI services on AWS using Lambda, SageMaker, Bedrock, S3, and related components built for scale, governance, and cost-efficiency.

• Lead the end-to-end development of RAG pipelines that connect internal datasets (e.g., logs, S3 docs, structured records) to inference endpoints using vector embeddings.

• Design and fine-tune LLM-based applications, including RAG using LangChain and other frameworks.

• Tune retrieval performance using semantic search techniques, proper metadata handling, and prompt injection patterns.

• Collaborate with internal stakeholders to understand business goals and translate them into secure, scalable AI systems.

• Own the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and Infrastructure as Code (Terraform).

• Support the development and evolution of reusable platform components for AI/ML operations.

• Create and maintain technical documentation for the team to reference and share with our internal customers.

• Exhibit excellent verbal and written communication skills in English.

REQUIRED KNOWLEDGE, SKILLS, AND ABILITIES

• 10+ years of proven software engineering experience, with strong focus on Python and GoLang and/or Node.js

• Demonstrated contributions to open-source AI/ML/Cloud projects, with either merged pull requests or public repos showing real usage (forks, stars, or clones)

• Direct, hands-on development of RAG, semantic search, or LLM-augmented applications using frameworks like Transformers, PyTorch, TensorFlow, and LangChain not just experimentation in notebooks

• Ph.D. in AI/ML/Data Science and/or named inventor on pending or granted patents in ML/AI

• Deep expertise with AWS services, especially Bedrock, SageMaker, ECS, and Lambda

• Proven experience fine-tuning LLMs, building datasets, and deploying ML models to production

• Demonstrated success delivering production-ready software with integrated release pipelines

NICE-TO-HAVES

• Policy as Code development (e.g., Terraform Sentinel) for managing automated cloud policy compliance

• FinOps mindset: experience optimizing cost-performance in AI systems

• Knowledge of data privacy and compliance best practices, including PII handling and secure model deployment

• Experience with AWS Organizations, SCP, and AWS Config